PicoScope 7 Automotive
Available for Windows, Mac, and Linux, the next evolution of our diagnostic scope software is now available.
PicoScope 7 Automotive
Available for Windows, Mac, and Linux, the next evolution of our diagnostic scope software is now available.
At Pico, we have distributors in over 50 countries. Find your local distributor here.
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Application Notes
Details of some of the many applications for our products.
Case Studies
Explore our case studies to see how organizations leverage our automotive solutions
Guided Tests
A library of examples on how to perform tests using PicoScope
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Knowledge Base
Everything you need to know about our products and software.
Training
A library of videos, articles, guides and information on how to use PicoScope
At Pico, we have distributors in over 50 countries. Find your local distributor here.
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Go to the how to order page to learn how to order Pico Technology productsLegal information
Go to the legal information page to learn more about the legal information of Pico TechnologyEnvironment & PicoPlanet
Go to the environment page to learn more about the environment and Pico Planetfrom airflow import DAG from airflow.operators.bash import BashOperator # Calls your compiled .NET binary task = BashOperator(task_id='run_dotnet', bash_command='dotnet /app/myConsumer.dll') The search for apacheniterydernet likely comes from a developer or architect wanting to unify:
| Apache Component | .NET Integration Method | Rider’s Role | |----------------|------------------------|--------------| | HTTP Server | Reverse proxy for ASP.NET Core | Edit, run, deploy configs | | Kafka | Confluent.Kafka client | Write producers/consumers + debug | | Spark | .NET for Apache Spark | Full C# job development | | Airflow | BashOperator + .NET DLLs | Python + .NET in one IDE | apacheniterydernet
var consumerConfig = new ConsumerConfig
static void Main() var spark = SparkSession.Builder().AppName("dotnet_spark_app").GetOrCreate(); DataFrame df = spark.Read().Text("input.txt"); var wordCounts = df.Select(Functions.Split(df["value"], " ").Alias("words")) .Select(Functions.Explode(df["words"]).Alias("word")) .GroupBy("word") .Count(); wordCounts.Show(); from airflow import DAG from airflow
using Microsoft.Spark.Sql; class Program DataFrame df = spark.Read().Text("input.txt")